This disclosure generally relates to data mining, and particularly relates to assessing multiple segmentation strategies for use in data mining.
A segmentation strategy partitions a population into several segments to achieve a better return on a decision making investment. In general, a segmentation strategy divides a heterogeneous group into homogenous subgroups, such as male and female groups, or other groups based on similar profiles, lifestyles, behavior, etc. Models are used for each segment of the segmentation strategy to predict a response, such as a response to a marketing activity. Predictive models may be designed for and trained for a given segment.
There are, however, many competing segmentation strategies as a large population may be segmented in numerous subgroups. For example, a segmentation strategy may be based on one or more of the following characteristics—gender, religion, income, ethnicity, etc. It is difficult to quantify and/or compare the performance of multiple segmentation strategies and determine which segmentation strategy offers the best performance.
A novel computer-implemented method and system for assessing segmentation strategies is disclosed herein. At least two models are selected for a plurality of segments. Segment performance of the segmentation strategy segments according to selected models is measured. Aggregate segmentation strategy performance data is obtained by aggregating segment performance for each segmentation strategy. Segmentation strategy performance indicia are generated to compare the aggregate segmentation strategy performance data of at least two of the segmentation strategies.